School of Energy and Power Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China.
J Hazard Mater. 2010 Feb 15;174(1-3):244-50. doi: 10.1016/j.jhazmat.2009.09.042. Epub 2009 Sep 16.
Mercury emission from coal combustion has become a global environmental problem. In order to accurately reveal the complexly nonlinear relationships between mercury emissions characteristics in flue gas and coal properties as well as operating conditions, an alternative model using support vector machine (SVM) based on dynamically optimized search technique with cross-validation, is proposed to simulate the mercury speciation (elemental, oxidized and particulate) and concentration in flue gases from coal combustion, then the configured SVM model is trained and tested by simulation results. According to predicted accuracy of indicating generalization capability, the model performance is compared and evaluated with the conventional multiple nonlinear regression (MNR) models and the artificial neural network (ANN) models. As a result, it is found that, the SVM provides better prediction performances with the mean squared error of 0.0095 and the correlation coefficient of 0.9164 for testing sample. Moreover, based on the SVM model, the correlativity between coal properties as well as operating condition and mercury chemical form is also analyzed in order to deeply understand mercury emissions characteristics. The result demonstrates that SVM can offer an alternative and powerful approach to model mercury speciation in coal combustion flue gases.
燃煤汞排放已成为全球性的环境问题。为了准确揭示烟气中汞排放特性与煤性质及运行条件之间复杂的非线性关系，提出了一种基于动态优化搜索技术和交叉验证的支持向量机(SVM)替代模型，用于模拟燃煤烟气中汞的形态(元素态、氧化态和颗粒态)和浓度，然后通过模拟结果对配置的 SVM 模型进行训练和测试。根据指示泛化能力的预测精度，将模型性能与传统的多非线性回归(MNR)模型和人工神经网络(ANN)模型进行比较和评估。结果表明，SVM 在测试样本上的均方误差为 0.0095，相关系数为 0.9164，具有更好的预测性能。此外，基于 SVM 模型，还分析了煤性质及运行条件与汞化学形态之间的相关性，以深入了解汞排放特性。结果表明，SVM 可以为模拟燃煤烟气中汞的形态提供一种替代的、强大的方法。
